Chapter 1 Motivation
5.2. Rainfall anomalies
AM2.1. Six experiments (noTopo control, noTopo 4xCO2, Topo control, Topo 4xCO2, U- ni4K and CMIP5SST) are performed (see more details in Table 5.2). These experiments have been designed to explore the impact of different regional forcings, such as land-sea con- trast, topography and SST distribution, on the EASM response. For instance, the difference between noTopo 4xCO2 and noTopo control is expected to show how enhanced land-sea thermal contrast influences regional precipitation without any contribution from topograph- ic forcing. These results can be compared with their counterparts with full topography. The difference between 4xCO2 and Uni4K or CMIP5SST is expected to show how SST patterns (in addition to SST uniform warming) affect the EASM. Climatological-fixed SSTs without internannual variability from monthly-mean Reynolds SST analysis are used as boundary condition (Smith et al. 1996). Each experiment ran for 25 years, and the last 14 years of the simulations are used for the analyses.
We analyze changes in the EASM precipitation and circulation just for the month of June, when most models well capture the EASM rainfall band. In doing so, we ignore possible changes in the EASM seasonality and only focus on seasonal mean changes in rainfall intensity and position.
Table 5.2 Experiments designed by using the GFDL-AM2.1.
Acronyms Descriptions Configurations
noTopo control Benchmark present-day simulation with no topography
Removed global topography, climatological SSTs, CO2
concentration 320 ppm noTopo 4xCO2
Evaluate impact on precipitation from enhanced land-sea contrast due to atmospheric CO2 forcing
without topographic forcing
Same as noTopo control but with CO2 concentration 1280 ppm
Topo control Benchmark present-day simulation with full topography
Retained global topography, climatological SSTs, CO2
concentration 320 ppm Topo 4xCO2
Evaluate impact on precipitation from enhanced land-sea contrast due to atmospheric CO2 forcing
with topographic forcing
Same as Topo control but with CO2 concentration 1280 ppm
Uni4K Evaluate impact of uniform increase in SSTs by 4K
Same with 4xCO2 but global SSTs are increased by 4K
everywhere CMIP5SST
Evaluate impact of increase in SSTs as evaluated from the
MMM in the CMIP5 slow response
Same as 4xCO2 but with anomalies in the slow response
from CMIP5 MMM added to global SST
and uncoupled simulations in EASM precipitation is fairly small (Fig. 5.1c, the spatial pat- tern and magnitude is consistent with a recent study by Song and Zhou 2014, their Fig. 8c) compared to that in either fast or slow response, safely concluding that air-sea interaction can be ignored and that the signal in Fig. 5.1d comes from the SST forcing in the MMM.
a. Fast Response
The fast response of the EASM rainfall band to elevated CO2 concentrations with fixed SSTs features a decrease (increase) of precipitation over oceanic (land) regions (Fig. 5.1b).
This precipitation response is robust in most models (not shown).
Anomalies in net precipitation (Fig. 5.2a) largely explain the pattern of precipitation change in the EASM (Fig. 5.1b), with changes in evaporation being important only over
Figure 5.1 Multi-model mean changes in precipitation (shading, W/m2) between different climate states and climatological precipitation (linear contour interval 1 mm/day, 3 – 9 mm/day) in each base state.
oceanic regions: here, the contribution by evaporation decreases along regions of large climatological evaporation (Fig. 5.2b). The spatial pattern of net precipitation change is consistent with changes in mean moisture flux convergence (Fig. 5.2c), although transient eddy flux anomalies, calculated as the residual of the moisture budget, are not negligible (Fig. 5.2d). Changes in mean moisture flux convergence are mainly captured by those due to winds (Fig. 5.2e). Contributions from changes in temperature (Fig. 5.2g), relative humidity (Fig. 5.2f), and their covariances (Fig. 5.2j-l) play a less important role. This confirms that in the absence of SST changes, the precipitation response is primarily dominated by changes in circulation, as seen in other tropical-subtropical regions (Bony et al. 2013a).
b. Slow response
At a first glance, changes in the slow response appear to follow the “wet get wet- ter” pattern. However, important deviations from the simple thermodynamic change exist (Fig. 5.3a): While the response is characterized by a well organized positive change in net
Figure 5.2 MMM anomalies (shading, W/m2) between sstClim4xCO2 and sstClim of net precipitation δ(P − E) (a), evaporation δE (b), mean flux convergence −hδ∇(v· Hqs)i (c), transient component (d, subtracted from a by c), wind component −h∇(δv·Hqs)i (e), relative humidity component −h∇(v·δHqs)i (f), temperature component −h∇(v·Hδqs)i (g), temperature component due to the Planck response (surface temperature) −αδTs(P − E) (h), temperature component due to lapse rate response (i, subtracted from g by h), covariance between relative humidity and wind −h∇(δv ·δHqs)i (j), covariance between relative humidity and temperature −hv· ∇(δHδqs)i (k), and covariance between wind and temperature−h∇(δv·Hδqs)i(l). Line contour (contour interval 1 mm/day, solid (dash) line means positive (negative) value) indicates climatological net precipitation in sstClim4xCO2 (a,c-l), climatological evaporation (b).
precipitation, this is located to the south of its climatological location. The net precipitation change over East China is negative, counteracting its positive change in the fast response.
Surface evaporation increases, particularly over oceanic regions where large evaporation re- ductions occur in the fast response (Fig. 5.3b). This increase in surface evaporation might be due to the experiment configuration: in sstClim4xCO2, SSTs are prescribed and surface evaporation is strongly limited; in abrupt4xCO2, SSTs are interactive, and a strong increase in local SSTs due to ocean dynamics might explain the narrow band of enhanced evaporation (Xie et al. 2010).
Figure 5.3 Same with Fig. 5.2 but for slow response.
The mean flux convergence, −hδ∇(v·Hqs)i, captures the overall spatial pattern of the net precipitation change in Fig. 5.3a, with strong moisture convergence on the southern flank of the rainfall band. Transient eddies show a significant contribution to the balance (Fig. 5.3d). Recall that because of the monthly resolution of the CMIP5 data, the transient eddy contribution is estimated from the moisture budget residual, which prevents a more careful mechanistic understanding of the transient eddy response. Changes due to winds (Fig. 5.3e) and temperature (Fig. 5.3g) are both important, with circulation changes domi- nating the overall spatial pattern, and temperature changes increasing moisture convergence over the climatological convergence zone. Contributions from relative humidity changes are nontrivial, but their magnitude and spatial extent are smaller than those from wind and temperature changes (Fig. 5.3f). As discussed in section 2, changes due to temperature can be decomposed into the Planck response (Fig. 5.3h) and the lapse rate response (Fig. 5.3i).
The Planck response relates the climatological net precipitation, weighted by the surface warming, to changes in net precipitation, or the so-called “wet get wetter” pattern. The
Planck response dominates the total response due to temperature, in both magnitude and spatial pattern. Weak signals over some land and oceanic regions are due to nearly zero cli- matological net precipitation, where local precipitation is primarily balanced by evaporation (c.f. Fig. 7a in Chen and Bordoni 2014). The coupling between temperature (saturation specific humidity) and wind changes (Fig. 5.3l) is dominant among the covariance terms (Fig. 5.3j-l) and resembles the dynamic change due to only winds (Fig. 5.3e). The reasoning is as follows: since temperature increases everywhere, the sign in the response is due to changes in winds, with specific humidity, (qs(T)), and specific humidity changes, (δqs(T)), acting as scaling factors.1
In both fast and slow responses, changes in circulation are significant and dominate the spatial pattern of the precipitation anomalies. Changes in thermodynamic quantities, such as temperature and relative humidity, play a less important role. Hence, we focus primarily on analyzing the local circulation changes, and infer possible mechanisms through which fundamental forcings, such as land-sea contrast, topography, and atmospheric CO2, affect local circulations directly or indirectly through larger-scale atmospheric circulation changes such as those of the NPSH.
Fig. 5.4 shows changes in precipitation and moisture flux due to changes in winds and geopotential height. Specifically, to clearly link geopotential height to circulation changes, in Fig. 5.4 we show differences in the local geopotential relative to the maximum value in the NPSH. This is because, through geostrophic balance, winds are linked to gradients in geopotential height rather than its magnitude. Additionally, geopotential heights tend to systematically shift upward under global warming. Our metric in Fig. 5.4 accounts for all of these factors.
On the larger scale, changes in the location and the strength of the NPSH in the fast
1A comparison between Figs. 5.3 e and l shows that qs(T) and δqs(T) are of similar magnitude. This is due to the nonlinear dependence ofqs(T) on temperature, which gives rises to big changes inqs(T) even for small changes in T. For instance, the water vapor saturation pressure is 3523 Pa at 300 K and 4701 Pa at 305 K, which implies that for only 5K difference in temperature, the water vapor saturation pressure differs by around 33%.
Figure 5.4 MMM anomalies of precipitation (shading, W/m2) due to winds, winds at 850 mb (vector, m/s), and difference in geopotential height between its maximum and locational value (line contour, contour interval 30 m, solid black, purple and brown lines indicate sstClim, sstClim4xCO2, and abrupt4xCO2, respectively) at 850 mb in the fast (a) and slow (b) responses. Short dash lines in black and purple indicate the inter-model spread (1 standard deviation) in sstClim and sstClim4xCO2 simulations.
response are within one standard deviation of the inter-model spread and therefore not signif- icant. In the slow response, instead, the NPSH moves southward and weakens significantly.
This implies that changes in winds over the EASM region are mostly local responses in the fast response, while resulting from a combination of local and remote responses, mediated
Figure 5.5 MMM anomalies of climatological moisture weighted wind convergence (shad- ing, W/m2) and vertical velocity at 500 mb (line contour, contour interval 0.005 Pa/s).
Solid/dash line indicates ascending/descending motion.
by the NPSH, in the slow response.
The dynamic moisture flux convergence anomalies, (−h∇ · q0δvi), can be further de- composed into a wind convergence component, (−hq0∇ ·δvi), and an advection component, (−hδv· ∇q0i). The wind convergence component, (−hq0∇ ·δvi), can be expressed in terms of the vertical advection using continuity, −hδω∂pq0i. The change in this term is largely explained by changes in vertical velocity at 500 mb (i.e., δω500, Fig. 5.5).
The vertical velocity is directly associated with remote forcing (i.e., energy advection), local radiative and surface fluxes, and stability. According to the MSE budget (Chen and
Bordoni 2014), vertical velocity can be approximated as the fraction between energy input and moist static stability. Here, we define a proxy for vertical velocity at 500 mb based on the MSE budget,
ω500apprx = −hv· ∇Ei+Fnet
−αh∂phi , (5.5)
where Fnet = St↓ −St↑ − Ss↓ +Ss↑ − R↑t +R↑s −R↓s +SH +LH, h = cpT +gz +Lvq is the MSE, E = cpT +Lvq is the atmospheric moist enthalpy, and Fnet is the net energy flux into the atmosphere, with the subscript t and s denoting the top of atmosphere and surface, respectively.2 α is a coefficient added to account for the coupling between vertical velocity and MSE stratification. Transient eddies are ignored and the coupling coefficient α is assumed to be homogeneous for simplicity. Fig. 5.6 shows changes in vertical velocity as diagnosed from the model output directly and from the approximation in Eq. 7.1 (i.e., δw and assuming α = 1).
At the first order, changes in vertical velocity can be partitioned into changes in ener- gy input and changes in stability (Appendix). Contributions from changes in energy input (mostly from horizontal advection of moist enthalpy) are significantly larger than those from changes in stability in both fast and slow responses (Fig. 5.7). In the fast response, anoma- lous positive moist enthalpy advection over Northeast China and negative moist enthalpy advection over the climatological rainfall band are closely associated with changes in verti- cal velocity. In the slow response, anomalies in moist enthalpy advection change sign, with anomalous positive moist enthalpy advection over ocean and negative advection over land.
Contributions from local stability are considerably smaller, however, with a destabilizing ef- fect over land in the fast response and over oceanic regions in the slow response. Anomalies in moist enthalpy advection are due to both dry enthalpy and latent energy advection, with similar spatial pattern (not shown) because of close relationship between temperature and water vapor changes via the Clausius-Clapeyron relationship.
2The vertical integration of moist static energy stratification is from 700 mb to 100 mb to account for the steepest slope for stability.
Figure 5.6 MMM anomalies of approximated vertical velocity ω500apprx (Eq. 7.1, shading, Pa/s) and MMM anomalies of climatological vertical velocity at 500 mb (line contour, con- tour interval 0.005 Pa/s). Solid/dash line indicates ascending/descending motion. ω500apprx is multiplied by a factor of 2 in the fast response (a).
Changes in the advection term (−hδv· ∇q0i) are a direct result from (mostly geostrophic) wind anomalies. In the fast and slow responses, changes in local precipitation over East China and adjacent oceanic regions are highly associated with meridional wind anomalies (Fig. 5.8 b, d). Intensified (weakened) meridional wind enhances (reduces) moisture transport, re- sulting in higher (lower) rainfall. In addition, the meridional component of the geostrophic flow on a β plane can induce convergent flow, which reinforces local precipitation in ad- dition to positive advective anomalies. Changes in meridional wind at 850 mb are largely
Figure 5.7 MMM anomalies of energy input (a and c, first term in Eq. 7.2, shading, W/m2), fractional changes in stability weighted by climatological energy input (b and d, second term in Eq. 7.2, shading, W/m2), and climatological vertical velocity at 500 mb (line contour, contour interval 0.005 Pa/s) in the fast (a and b) and slow (c and d) responses. Solid/dash line indicates ascending/descending motions.
height (Z850) gradient anomalies. For simplicity, ignoring subtle influences from changes in the atmospheric temperature between the surface and 850 mb pressure level, Z850 is only dependent on ln(ps), wherepsindicates surface pressure. Anomalies in locational differences in surface pressure, i.e., δ ln(ps1/ps2) will change the Z850 gradient, and thereafter create wind anomalies, δv850. In the fast response, enhanced land-sea contrast is manifest in an increased surface pressure gradient, with lower pressure over land and higher pressure over ocean. Meridional wind is subsequently enhanced. In the slow response, however, land-sea contrast is weakened, and the meridional wind is reduced. This relationship is well observed amongst different model simulations (Fig. 5.9).3 Changes in precipitation over the oceanic rainfall band, however, are largely due to changes in zonal wind, particularly in the slow response (Fig. 5.8 c). Enhanced lower-level westerly wind might be related to a southward displacement of the NPSH. In the fast response, the NPSH does not feature significant changes in its spatial pattern, which might be the reason why contributions from anomalous advection of climatological moisture are limited.
3The robustness of the relationship between δ ln(ps1/ps2) and δv850 is insensitive to the width of the region we choose (the East boundary varies from 130E to 140E).
Figure 5.8 Zonal (a, c) and meridional (b, d) components of MMM anomalies of climato- logical moisture advection at 850 mb (shading, W/m2)) in the fast (a, b) and slow (c, d) responses.
c. Summary
We have diagnosed precipitation changes in the EASM region in both fast and slow responses. Some robust conclusions emerging from this diagnosis include:
• Changes in net precipitation are associated with changes in the moisture flux conver- gence, which is dominated by the dynamic component (i.e., by changes in circulation);
• The wind convergence term in the dynamic component is directly linked to changes in vertical velocity through continuity;
• These changes in vertical velocity are found to be mostly related to changes in moist enthalpy advection, with changes in vertical stability playing a lesser role;
• Changes in horizontal moisture advection over East China are dominated by changes in the meridional wind, which is a consequence of changes in land-sea contrast. The zonal component dominates the slow response over the oceanic regions, as a possible consequence of the southward displacement of the NPSH.
Figure 5.9 Scatterplot (blue/red for fast/slow reponse) of meridional wind anomaly over East China and adjacent oceans (25N-40N, 110E-130E) and surface pressure gradient anomaly between land (100E-120E) and ocean (130E-150E) over 25N-40N band. Each dot represents one model output as indicated in Table 5.1. Solid line indicates linear regression line in fast/slow response, respectively. See text for more details.